Rethinking Remoteness: A Simple and Objective Approach
ZHAO YUEJEN
Adjunct Senior Research Fellow
Institute of Advanced Studies
Charles Darwin University
GUTHRIDGE STEVEN
Adjunct Principal Research Fellow
Institute of Advanced Studies
Charles Darwin University
Postal Address:
Health Gains Planning, 4th Floor, AANT Centre
DHCS, PO Box 40596
Casuarina NT 0811
Email address:
yuejen.zhao@nt.gov.au
1
Rethinking Remoteness: A Simple and Objective Approach
Abstract
This paper re-examines the characteristics and assumptions of current
remoteness/accessibility classifications in Australia and proposes a simple and
easily understandable alternative measure for remoteness. In this study,
remoteness is redefined simply as “the average distance between two nearest
people within an appropriate spatial unit where population distribution is
assumed to be homogenous”. By definition, the most straightforward
remoteness and incapacity index (RII) would be remoteness times a measure of
the incapacity for social and commercial interaction, where remoteness is
gauged by the square root of the area divided by the population, and incapacity
is measured by the reciprocal of population.
Australian Bureau of Statistics Statistical Local Area (SLA) level population data
and digital boundaries have been utilised for assessment of this index. The
utility of the RII is demonstrated with two examples of activity measures for
general practitioner services and businesses. At the State/Territory level, RIIs
are negatively related to both general practitioner services per person (Pearson
correlation coefficient r=-0.873), and the number of businesses per person (r=0.546). The correlation can be further enhanced by normalising the distributions
of the remoteness scores with a simple logarithmic function. The strong
correlations confirm that remoteness has a substantial inverse impact on daily
activities. Greater distance means longer time and higher costs for travelling,
2
diseconomy of scale, and higher personnel costs. The RII provides an
alternative measure of remoteness that is both intuitive and statistically
straightforward, and at an SLA level, closely coincides with the commonly used
but complex Accessibility/ Remoteness Index of Australia Plus (ARIA+).
Significantly the RII is free of the service specific and policy sensitive
adjustments justified by “accessibility” that have been introduced into existing
measures.
Key words: Remoteness, Population Density, Geographical Classifications,
ARIA, ASGC, Accessibility.
Acronyms
ABS
Australian Bureau of Statistics
ACT
Australian Capital Territory
ARIA
Accessibility /Remoteness Index of Australia
ARIA+
Accessibility/ Remoteness Index of Australia Plus
ASGC
Australian Standard Geographical Classification
CD
Collection Districts
GISCA
National Centre for Social Applications of Geographic Information
Systems
GP
General practitioner
MDD
Mean distance deviation
NSW
New South Wales
NT
Northern Territory
3
QLD
Queensland
RDR
Relative dispersion ratio
RII
Remoteness and incapacity index
RRMA
Rural, Remote and Metropolitan Areas classification
SA
South Australia
SARIA
State based ARIA
SLA
Statistical Local Area
TAS
Tasmania
VIC
Victoria
WA
Western Australia
Introduction
This paper re-examines the assumptions and characteristics of current
remoteness/accessibility classifications in Australia and proposes a simple and
easily understandable alternative measure for remoteness. It is critical that
agencies that operate in rural and remote areas have a transparent and
objective remoteness measure to inform equitable resource management and
service delivery.
In the early 1970s, Holmes (1973) applied a range of key geographical
measures developed overseas (Bachi, 1962; Neft, 1966) to investigate
population dispersion for Australian States. Subsequently, Faulkner and French
(1983) developed a remoteness index by measuring the distances from 702 grid
squares of Australia to the closest urban centres and transforming the distances
4
to scores of a standardised normal distribution. In 1987, Holmes (1988) defined
remoteness by using area divided by population (in km2 per person) and found
it was highly correlated with the Faulkner and French index, but with an
algorithm that was significantly simpler. In a later development (Department of
Primary Industries and Department of Human Services and Health, 1994), the
Rural, Remote and Metropolitan Areas classification (RRMA) was released as a
remoteness classification based on 1991 Census and Statistical Local Area
(SLA) boundaries. The RRMA classification introduced the additional concept of
“accessibility” by the measurement of distance from an SLA to the nearest
centre in each of three zones - metropolitan, rural and remote. In the same
year, Griffith (1994) developed a detailed methodology for quantifying
accessibility to a particular service or a defined group of services by combining
population, economic resources and a composite element of distance, time and
cost.
In 1999, the National Centre for Social Applications of Geographic Information
Systems (GISCA) completed a commission by the Australian Department of
Health and Aged Care to develop a new classification system, the Accessibility
/Remoteness Index of Australia (ARIA)(Dunne et al, 1999). The ARIA approach
had four steps: first, define four categories of service centres based on
subjective thresholds of population size; second, measure the road distances
from any particular point to the nearest four centres; third, divide the distance by
the mean and truncate the maximum score at 3; and finally, aggregate the
scores to a single index with a maximum value of 12. This approach results in
5
five categories of regions: highly accessible, accessible, moderately accessible,
remote and very remote (Department of Health and Aged Care, 2001). Since
then the classification has been further modified for specific purposes, for
example ARIA+ and State based ARIA (SARIA). ARIA+ adopted five categories
of service centres and therefore used a spread of scores 1-15, whereas SARIA
maintained the ARIA+ algorithm except that it measured road distance from
each populated locality to the nearest service centre in the same State/Territory
(GISCA, 2004). The Australian Bureau of Statistics (ABS) has adopted ARIA+
as a remoteness classification (ABS, 2003).
For ARIA, remoteness has been defined in terms of distance of a location from
service or population centres (Department of Health and Aged Care, 2001).
Importantly the definition does not consider the distances between individuals or
between service centres. In this study, “remoteness” is redefined simply as “the
average distance in space between two nearest people within an appropriate
spatial unit where population distribution is assumed to be homogenous”. This
measurement directly reflects the ease with which people can interact. The
greater the average distance between people, the more remote is the area, the
fewer the opportunities for social interaction and the less opportunities for both
supply and demand. This definition for remoteness consists of only two
elements, distance in space and number of persons. Remoteness is a
characteristic of geographic localities in relation to their human inhabitants.
Apart from distance, the ability to travel and interact is also clearly reliant on
6
infrastructure, such as roads, public transport and communication, but these
factors are themselves a direct consequence of population size.
Accessibility is a concept closely related to remoteness, but is deliberately
excluded in the proposed measure for “ remoteness”. The reason is that once
accessibility is incorporated into a remoteness measure, the measure is no
longer simple and more importantly cannot be consistently generalised.
Accessibility is highly influenced by “availability” and can only be defined in
relation to a specific service. Without a clear definition of type of service,
accessibility is unquantifiable (Griffith, 1994; 2002). In distinguishing service
access models from geographic models, Griffith (2007) has recently defined the
separate elements for both types of classification.
Method
Analogous to Holmes (1988), this study proposes a microgeographic approach
to population density as a measure for remoteness. By definition, the most
straightforward remoteness and incapacity index (RII) would be remoteness
times incapacity, where remoteness is gauged by the square root of area
divided by population, and incapacity is measured by the reciprocal of
population in thousands. Thus,
RII=a1/2p-3/2
(1)
where a=area and p=population with a>0, p>0 and RII> 0, ranging from least
remote (close to 0) to most remote (much greater than 0).The use of the
square root transforms area to distance, with area assumed to be square. The
7
RII represents the average distance that people have to travel over the capacity
for interaction when, for example, a service provider delivers a service to a
client. The RII is directly proportional to per capita average travelling distance
for the area and inversely proportional to the size of the resident population.
The population distribution is assumed to be homogeneous within the area of
interest.
The RII is a different concept from Holmes’ measure of dispersion (Holmes,
1973), which measures the mean distance deviation (MDD) of the population
from the median centre (intersection of the population middle values along the
orthogonal axes) or mean centre. A related measure, the relative dispersion
ratio (RDR), is the standard distance deviations for the population and the area,
and is used to offset the affects of area size and population density. Holmes
applied dispersion to describe population concentration. For example, the
United Kingdom (RDR=0.86) was reported as more dispersed and less
concentrated than Australia (0.72) (Holmes, 1973). This result highlights the
difference between dispersion and remoteness and do not indicate that the
United Kingdom is more remote than Australia.
Optimally, a remoteness measure would be: transparent and simple to
calculate; easy to aggregate and disaggregate to cover all levels of spatial units;
scientifically plausible; continuous, informative and comprehensive. The
measure also needs to be based on a minimum of assumptions and be free of
subjective manipulation.
8
Simplicity: This approach simply uses the average distance in kilometres per
person as the basis to quantify remoteness adjusted by incapacity. It can be
applied using a range of data sources, including publicly available ABS
population and area data for SLA or census Collection Districts (CD). There are
no difficulties in calculating RII and comparing it with other areas. The algorithm
is transparent and easy to understand.
Aggregation: RII can be easily aggregated to a higher spatial or administrative
unit without the need for weighting, and disaggregated to a lower spatial unit
without losing comparability with neighbouring areas.
Continuity: RII is a continuous geographical measure for remoteness. It can be
applied to any level of geography, including geocoded localities. By defining a
centroid with appropriately sized area (for example, 1010 km2) as an
information capturing grid cell and scanning the whole of Australia, every
locality has an RII value. For convenience, this study calculates the RII values
using the existing Australian Standard Geographical Classification (ASGC)
digital boundaries (CDs, SLAs and State/Territory), and ABS SLA level
population data (ABS, 2002).
(Insert Table 1 here)
9
Results
The RII values for each Australian State and Territory are presented in Table 1.
The Northern Territory (NT) is the most remote with an RII value of 13.057,
followed by Tasmania (TAS), Western Australia (WA) and South Australia (SA).
The Australian Capital Territory (ACT) outweighs Queensland (QLD), because
of the small population and less capacity. New South Wales (NSW) and Victoria
(VIC) are the least remote States with RII values less than 0.1. MDD and RDR
are also presented using 2001 census data updates for comparison in Table 1.
NT is ranked first by MDD and second by RDR in terms of dispersion, while the
MDD and RDR are inconsistent in determining the least dispersed State/
Territory. Figure 1 shows the RII values mapped by SLA.
The application of RII can be simplified by categorising the scores into six broad
conventional regions: urban (0<RII <0.002), suburban (0.002≤RII <0.008),
regional (0.008≤RII <0.05), rural (0.05≤RII <0.5), remote (0.5≤RII <2), and very
remote (RII ≥2). The population proportions for these categories, calculated at
an SLA level are presented for each State and Territory in Table 2.
(Insert Figure 1 here)
Figure 1 Remoteness and incapacity index defined regions mapped by
Statistical Local Areas (ASGC 2001)
(Insert Table 2 here)
10
The utility of the RII was tested in two contrasting examples by Pearson
correlation between RII and activity. For the first example, general practitioner
(GP) annual per capita services data by State/Territory between 2000-01 and
2005-06 was obtained from Medicare Australia’s online database (Medicare
Australia, 2007). It was shown that the annual per capita GP services and RII
were negatively correlated (Pearson correlation coefficient r=–0.873). This
indicates the more remote and less capacity, the more difficult it is for residents
to utilise Commonwealth funded primary care medical services. Model
diagnostics showed the NT was both outlying and influential. To increase the
robustness of the model, the logarithm of RII was used. Consequently, the
correlation coefficient has increased substantially (r=-0.908).
The calculation for this example highlighted the wide variation of scores and
that an adjusted index with a normalised distribution could be estimated in the
form of k+log(a1/2p-3/2), where k can be estimated by solving the following
equation with a known travelling cost relativity C:




k  max log( a1/2 p 3/2 )
C
.
k  min log( a1/2 p 3/2 )
(2)
This equation can be rewritten as
k




max log(a1/2 p 3/2 )  C  min log(a1/2 p 3/2 )
.
C 1
(3)
At the State/Territory level, using the distance ratio in a1/2p-1/2 between the most
and least remote State/Territory as a surrogate measure for the cost relativity, it
11
follows that C=12 in rounded terms, and then k=1.58. The resulting cost
adjusted RII values are listed in the last column of Table 1.
For the second validating example, we utilised data on the number of
businesses per person (ABS, 2001). Pearson correlation and regression were
again used for analysing the relation between remoteness and business activity.
It was found that the cost adjusted RII and the number of businesses per
person was moderately related (r=-0.546). Again it is indicative that the more
remote the area, the lower the business activity.
(Insert Figure 2 here)
Figure 2 Correlation between RII and ARIA+ scores by Statistical Local Areas,
Australia, 2001
The RII is also consistent with existing but more complex measures. The ARIA+
scores by SLA were obtained from the GISCA website (GISCA, 2007) and
merged with the RII data. At the SLA level, it is estimated that C=138 and
k=4.00. A set of cost adjusted RII values were derived and a scatter diagram
was plotted to study the relationship between ARIA+ and the adjusted RII
values (Figure 2). It revealed that at the SLA level, adjusted RII and ARIA+
were closely correlated (r=0.700), after excluding those SLAs for which ARIA+
scores are ‘null’. If SLAs with a population less than 100 are excluded, r
increases to 0.747. If SLAs with a population less than 500 are excluded, r
increases only marginally to 0.749. The analysis also highlighted that ARIA+
scores were artificially concentrated around 0 and 3. Further scrutiny of Figure 2
12
suggested that RII outlying localities were likely new/outskirt rural suburbs or
national parks with small population and relatively large area (eg. Willawong
ARIA+ 0:RII 3.8, Pialligo and Yarra Ranges (B) 0.1:4.3, and Majura 0.2:4.4), but
ARIA+ outlying localities were more likely remote mining or Indigenous
townships with relatively large population and small geographical area (eg.
Nhulunbuy 12:2.1, Torres 15:2.6, Weipa 12:2.5 and Tennant Creek 12.1:2.6).
(Insert Table 3 here)
Discussion
The impacts of greater distance on service delivery need to be carefully
considered. Greater distance means longer time and higher costs for travelling,
diseconomy of scale and resulting inefficiency, and higher personnel costs. The
distance restricts opportunities for interaction. Comparing Table 2 with existing
classifications in Table 3, the RII results are broadly consistent with SARIA. The
algorithm of RII is however simpler, more transparent and is derived from
readily available information. An appropriate approach to solving remote area
accessibility issues should be to first recognise geographically remote areas,
and then tackle access barriers within those areas. The new approach
separates the measurement of remoteness as a continuous variable measured
in terms of human inhabitants from the complexities of estimating accessibility.
Accessibility cannot be measured solely by road distances. Different people in
the same location can experience different degrees of service accessibility
13
according to factors such as their income, personal mobility, attitudes, language
and the services they perceive as more important. The new method is an
unambiguous geographic and population approach to measuring remoteness,
whereas ARIA, ARIA+ and SARIA methods attempt a mixed model for both
remoteness and accessibility.
The ARIA systems are policy driven as articulated by Dunne et al (1999), and
those policy-driven assumptions distort the classification. The ARIA scores have
a subjective maximum cut-off value and as result the true remoteness of a large
number of CDs in Northern and Central Australia are arbitrarily understated.
Without justification, SARIA treats Darwin and Hobart as having the same
capacity to deliver services as Sydney and Melbourne. SARIA relies on arbitrary
judgements when choosing service centres for a specific locality. The
complicated algorithms in the current existing classifications are open to
misjudgement and manipulation. The service centre approach deals with
islands by applying distinct weightings based on distance. By contrast, the RII is
a self-referenced classification and estimates remoteness for islands and the
continent in a consistent manner without the need for adjustments. Unlike
existing classifications, the RII approach minimises the use of arbitrary
judgements and assumptions.
The ARIA scores have been ‘standardised’ and “distances” converted to scores.
When choosing or changing over to a different set of service centres, the
existing classifications produce totally different and discrete remoteness scores.
14
The RII measures can be easily aggregated to a higher areal unit or separated
to lower areal units. It does not involve weightings to combine or disaggregate.
In consonance with ARIA, RII allows remoteness to be calculated for any level
of geography including towns, postcodes, SLAs, CDs, grid cells and userdefined areas. The RII measures are continuous. ARIA does not need to have
population at the location for which remoteness value is derived, while RII must
be based on an appropriately sized area, and the population in this area must
be greater than zero.
Comparison of RII with MDD and RDR demonstrated RII appeared more
plausible than MDD and RDR. According to Holmes (1973), neither MDD nor
RDR fully took population density into consideration.
Two major factors limit the precision of our findings. Firstly, aggregated areal
data have been used for correlation analysis, which may have overstated the
true connection between remoteness and activities at an individual level
(Openshaw, 1982). Openshaw analysed a subset of census data (from the
United Kingdom and Italy) and demonstrated that an areal aggregate correlation
of 0.8-1.0 corresponded to a much weaker individual correlation of 0.4-1.0.
Openshaw also acknowledged that without accessing individual unit records, it
was difficult to determine individual correlation. If geocoded de-identified unit
records are available, multilevel models (Hox, 1995) may be useful to analyse
the hierarchically structured data. Secondly, the RII was developed on SLA
based geographical and population data, and incapable of differentiating
15
smaller spatial units within SLA in this study. SLAs were the smallest spatial
units defined by ABS on the administrative areas of local governments for data
collection, dissemination at intercensal times and aggregation to a larger spatial
unit (ABS, 2007). With introduction of Mesh Blocks (ABS, 2003), future analytic
areal units will become smaller and more homogenous in population
distribution. Using the new geographic information technology to define
appropriately sized grid cells (approximately 1010 km2), the map of Australia
can be scanned and the RII values for every dot point on the map can be
estimated and compared. However, when the population density is extremely
low (for example population is less than 100 for an SLA), the RII values tend to
be dominated by the size of the area. In this case, the area needs to be merged
with the neighbouring area or the size of the areas needs to be standardised.
When the total population is high and the area is large, the RII values tend to be
dominated by the incapacity component. In this case, the area could be split to
smaller areal units, provided the necessary information is available. The RII
classification appears to have many benefits over existing classifications and its
utility justifies consideration.
Acknowledgements
The authors acknowledge the constructive comments provided by Dr Dennis
Griffith and Dr Hock Seng Lee on earlier versions of this manuscript. We would
like to thank our reviewers for their insights.
References
16
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19
Table 1 Remoteness and incapacity index (RII) by State/Territory, 2005
Population
(persons)
Area (km2)
RII
NSW
6 575 217
801 349
VIC
4 804 726
QLD
Dispersion
Cost Adjusted
RII
0.052
MDD
119.3
RDR
0.316
227 594
0.044
59.3
0.444
0.22
3 628 946
1 734 157
0.182
273.6
0.393
0.84
WA
1 901 159
2 532 423
0.590
134.9
0.176
1.30
SA
1 511 728
985 341
0.529
59.9
0.109
1.35
TAS
471 795
67 914
0.793
99.8
0.950
1.48
ACT
319 317
2 349
0.266
9.6
0.446
1.00
NT
197 768
1 352 158
13.057
369.1
0.659
2.69
0.30
Note: MDD=mean distance deviation, RDR=relative dispersion ratio, updated using 2001 population (Holmes, 1973)
Table 2 Remoteness and Incapacity Index defined regions and population
shares (%) by State/Territory, 2005
Urban
Suburban
Regional
Rural
NSW
75.6
10.7
6.9
6.2
0.6
0.0
100
VIC
70.1
15.4
7.7
6.7
0.2
0.0
100
QLD
25.4
45.0
18.6
8.6
1.7
0.7
100
WA
69.2
11.9
5.7
8.0
3.8
1.5
100
SA
63.5
11.6
13.8
9.0
1.7
0.2
100
TAS
42.4
23.7
15.8
16.8
1.3
0.0
100
ACT
5.3
58.2
34.3
1.8
0.0
0.4
100
NT
0.0
23.6
49.1
8.2
12.9
6.3
100
60.5
19.7
10.8
7.4
1.3
0.4
100
Australia
Remote Very Remote
Total
20
Table 3 Population shares by some existing remoteness classifications
Proportion (%) of population by State/Territory
Region
NSW
VIC
QLD
WA
SA
TAS
ACT
NT Australia
Capital
62
70
45
73
73
41
99
45
63
Other metro
13
4
13
0
0
0
0
0
8
Large rural
5
4
14
0
2
19
0
0
6
Small rural
8
5
6
7
5
11
0
0
6
Other rural
11
16
17
10
18
28
1
8
14
Remote centre
0
0
3
5
0
0
0
19
1
Other remote
1
0
3
5
2
1
0
28
2
Total
100
100
100
100
100
100
100
100
100
Major cities
71
74
52
70
72
0
100
0
66
Inner regional
21
21
26
12
12
64
0
0
21
Outer regional
8
5
18
10
12
34
0
52
11
Remote
1
0
3
5
3
2
0
22
2
Very remote
0
0
2
4
1
1
0
25
1
Total
100
100
100
100
100
100
100
100
100
Highly accessible
70
74
54
70
72
35
100
48
68
Accessible
15
21
25
11
14
57
0
4
18
Moderately
14
5
17
10
11
7
0
7
11
Remote
accessible
Very remote
1
0
3
2
2
0
0
18
2
0
0
2
6
1
1
0
22
1
Total
100
100
100
100
100
100
100
100
100
RRMA (a)
ARIA+ (b)
SARIA (b)
(a) Commonwealth Grants Commission, 1999. (b) Commonwealth Grants Commission, 2004.
21